code: def _no_grad_context(loop_run: Callable) -> Callable:
      def _decorator(self: _Loop, *args: Any, **kwargs: Any) -> Any:
          if not isinstance(self, _Loop):
              raise TypeError(
                  f"`{type(self).__name__}` needs to be a Loop."
              )
          if not hasattr(self, "inference_mode"):
              raise TypeError(
                  f"`{type(self).__name__}.inference_mode` needs to be defined"
              )
          context_manager: Type[ContextManager]
          if (
              _distributed_is_initialized()
              and dist.get_backend() == "gloo"
          ):  # noqa: SIM114
              # gloo backend does not work properly.
              # https://github.com/Lightning-AI/lightning/pull/12715/files#r854569110
              # TODO: explore why and possibly open an issue in PyTorch repository
              context_manager = torch.no_grad
          elif isinstance(
              self.trainer.accelerator, XLAAccelerator
          ):  # noqa: SIM114
              context_manager = torch.no_grad
          elif _TORCH_GREATER_EQUAL_1_13 and isinstance(
              self.trainer.strategy, FSDPStrategy
          ):  # noqa: SIM114
              # https://github.com/pytorch/pytorch/issues/95957
              context_manager = torch.no_grad
          elif (
              _TORCH_EQUAL_2_0
              and self.trainer.lightning_module._compiler_ctx
              is not None
          ):
              # avoid: `RuntimeError: Inference tensors do not track version counter` fixed in v2.1
              context_manager = torch.no_grad
          elif self.inference_mode:
              context_manager = torch.inference_mode
          else:
              context_manager = torch.no_grad
          with context_manager():
              return loop_run(self, *args, **kwargs)
  
      return _decorator
  
imports: import inspect
  from contextlib import contextmanager
  from typing import Any, Callable, ContextManager, Generator, Optional, Tuple, Type
  import torch
  import torch.distributed
  from torch import Tensor
  import lightning.pytorch
  from lightning.fabric.utilities.distributed import _distributed_is_initialized
  from lightning.fabric.utilities.imports import _TORCH_EQUAL_2_0, _TORCH_GREATER_EQUAL_1_13
  from lightning.fabric.utilities.warnings import PossibleUserWarning
  from lightning.pytorch.accelerators.xla import XLAAccelerator
  from lightning.pytorch.callbacks.timer import Timer
  from lightning.pytorch.loops import _Loop
  from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher, _PrefetchDataFetcher
  from lightning.pytorch.loops.progress import _BaseProgress
  from lightning.pytorch.strategies import FSDPStrategy
  from lightning.pytorch.strategies.parallel import ParallelStrategy
  from lightning.pytorch.strategies.strategy import Strategy
  from lightning.pytorch.trainer.states import RunningStage
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
label_desc: Use image node_id 7 for calling a global function with example usage: _no_grad_context(loop_run) and returns: _decorator, loop_run